Competitive swarm optimizer for Solving Flexible Jobshop Scheduling Problem

Abstract

F1exible job shop scheduling problem (FJSP) is an extension of job shop scheduling problem (JSP) that has received increasing attention in recent decades. FJSP is a high-dimensional combinatorial optimization problem. Using accurate algorithms to solve them is a challenge and costly. The difference is that a meta-heuristic algorithm is an algorithm based on intuition or experience that gives a feasible solution to the problem at an acceptable cost (referring to calculation time and space). Particle Swarm optimization (PSO) is a classic meta-heuristic algorithm that has achieved many successful applications. However, it is easy to converge prematurely when solving high-dimensional problems. Competitive Swarm optimizer (CSO), as a variant of particle swarm optimization, has excellent global search capabilities to deal with high-dimensional problems. Therefore, this article uses CSO to solve FJSP. We introduced five other algorithms as a comparison to verify our algorithm. Numerical comparison results show that CSO can optimize all FJSP better overall.